{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) 2020, NVIDIA CORPORATION.\n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "you may not use this file except in compliance with the License.\n",
    "You may obtain a copy of the License at\n",
    "    http://www.apache.org/licenses/LICENSE-2.0\n",
    "Unless required by applicable law or agreed to in writing, software\n",
    "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "See the License for the specific language governing permissions and\n",
    "limitations under the License."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, time\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
    "start = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd, numpy as np, gc\n",
    "from datetime import datetime\n",
    "import joblib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.max_rows', 500)\n",
    "import cudf, cupy, time\n",
    "cudf.__version__\n",
    "\n",
    "startNB = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numba import jit, njit, prange\n",
    "from sklearn.metrics import precision_recall_curve, auc, log_loss\n",
    "\n",
    "def compute_prauc(gt, pred, nafill=True):\n",
    "    if nafill:\n",
    "        pred[ np.isnan(pred) ] = np.nanmean( pred )\n",
    "    prec, recall, thresh = precision_recall_curve(gt, pred)\n",
    "    prauc = auc(recall, prec)\n",
    "    return prauc\n",
    "\n",
    "@jit\n",
    "def fast_auc(y_true, y_prob):\n",
    "    y_true = np.asarray(y_true)\n",
    "    y_true = y_true[np.argsort(y_prob)]\n",
    "    nfalse = 0\n",
    "    auc = 0\n",
    "    n = len(y_true)\n",
    "    for i in range(n):\n",
    "        y_i = y_true[i]\n",
    "        nfalse += (1 - y_i)\n",
    "        auc += y_i * nfalse\n",
    "    auc /= (nfalse * (n - nfalse))\n",
    "    return auc\n",
    "\n",
    "@njit\n",
    "def numba_log_loss(y,x):\n",
    "    n = x.shape[0]\n",
    "    ll = 0.\n",
    "    for i in prange(n):\n",
    "        if y[i]<=0.:\n",
    "            ll += np.log(1-x[i] + 1e-15 )\n",
    "        else:\n",
    "            ll += np.log(x[i] + 1e-15)\n",
    "    return -ll / n\n",
    "\n",
    "def compute_rce(gt , pred, nafill=True, verbose=0):\n",
    "    if nafill:\n",
    "        pred[ np.isnan(pred) ] = np.nanmean( pred )\n",
    "        \n",
    "    cross_entropy = numba_log_loss( gt, pred  )\n",
    "    \n",
    "    yt = np.mean(gt>0)     \n",
    "    strawman_cross_entropy = -(yt*np.log(yt) + (1 - yt)*np.log(1 - yt))\n",
    "    \n",
    "    if verbose:\n",
    "        print( \"logloss: {0:.5f} / {1:.5f} = {2:.5f}\".format(cross_entropy, strawman_cross_entropy, cross_entropy/strawman_cross_entropy))\n",
    "        print( 'mean:    {0:.5f} / {1:.5f}'.format( np.nanmean( pred ) , yt  ) )\n",
    "    \n",
    "    return (1.0 - cross_entropy/strawman_cross_entropy)*100.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_memory( df ):\n",
    "    features = df.columns\n",
    "    for i in range( df.shape[1] ):\n",
    "        if df.dtypes[i] == 'uint8':\n",
    "            df[features[i]] = df[features[i]].astype( np.int8 )\n",
    "            gc.collect()\n",
    "        elif df.dtypes[i] == 'bool':\n",
    "            df[features[i]] = df[features[i]].astype( np.int8 )\n",
    "            gc.collect()\n",
    "        elif df.dtypes[i] == 'uint32':\n",
    "            df[features[i]] = df[features[i]].astype( np.int32 )\n",
    "            gc.collect()\n",
    "        elif df.dtypes[i] == 'int64':\n",
    "            df[features[i]] = df[features[i]].astype( np.int32 )\n",
    "            gc.collect()\n",
    "        elif df.dtypes[i] == 'float64':\n",
    "            df[features[i]] = df[features[i]].astype( np.float32 )\n",
    "            gc.collect()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TARGET_id = 0\n",
    "\n",
    "TARGETS = ['reply', 'retweet', 'retweet_comment', 'like']\n",
    "\n",
    "TARGET = TARGETS[TARGET_id]\n",
    "\n",
    "train = pd.read_parquet( 'data/train-final-te-'+TARGET+'-1.parquet' )\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['a_follower_count']  = train.groupby('a_user_id')['a_follower_count' ].transform('last');_ = gc.collect()\n",
    "train['a_following_count'] = train.groupby('a_user_id')['a_following_count'].transform('last');_ = gc.collect()\n",
    "train['b_follower_count']  = train.groupby('b_user_id')['a_follower_count' ].transform('last');_ = gc.collect()\n",
    "train['b_following_count'] = train.groupby('b_user_id')['a_following_count'].transform('last');_ = gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hashtags</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>media</th>\n",
       "      <th>links</th>\n",
       "      <th>domains</th>\n",
       "      <th>tweet_type</th>\n",
       "      <th>language</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>a_user_id</th>\n",
       "      <th>a_follower_count</th>\n",
       "      <th>a_following_count</th>\n",
       "      <th>a_is_verified</th>\n",
       "      <th>a_account_creation</th>\n",
       "      <th>b_user_id</th>\n",
       "      <th>b_follower_count</th>\n",
       "      <th>b_following_count</th>\n",
       "      <th>b_is_verified</th>\n",
       "      <th>b_account_creation</th>\n",
       "      <th>b_follows_a</th>\n",
       "      <th>reply</th>\n",
       "      <th>retweet</th>\n",
       "      <th>retweet_comment</th>\n",
       "      <th>like</th>\n",
       "      <th>id</th>\n",
       "      <th>tr</th>\n",
       "      <th>dt_day</th>\n",
       "      <th>dt_dow</th>\n",
       "      <th>dt_hour</th>\n",
       "      <th>a_count_combined</th>\n",
       "      <th>a_user_fer_count_delta_time</th>\n",
       "      <th>a_user_fing_count_delta_time</th>\n",
       "      <th>a_user_fering_count_delta_time</th>\n",
       "      <th>a_user_fing_count_mode</th>\n",
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       "      <th>a_user_fering_count_mode</th>\n",
       "      <th>count_ats</th>\n",
       "      <th>count_char</th>\n",
       "      <th>count_words</th>\n",
       "      <th>tw_hash</th>\n",
       "      <th>tw_freq_hash</th>\n",
       "      <th>tw_first_word</th>\n",
       "      <th>tw_second_word</th>\n",
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       "      <th>TE_b_user_id_tweet_type_language_reply</th>\n",
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       "      <th>TE_a_user_id_reply</th>\n",
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       "      <th>TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_reply</th>\n",
       "      <th>TE_a_count_combined_tweet_type_language_reply</th>\n",
       "      <th>TE_a_user_fer_count_delta_time_media_language_reply</th>\n",
       "      <th>TE_a_user_fing_count_delta_time_media_language_reply</th>\n",
       "      <th>TE_a_user_fering_count_delta_time_tweet_type_language_reply</th>\n",
       "      <th>TE_a_user_fing_count_mode_media_language_reply</th>\n",
       "      <th>TE_a_user_fer_count_mode_media_language_reply</th>\n",
       "      <th>TE_a_user_fering_count_mode_tweet_type_language_reply</th>\n",
       "      <th>TE_domains_media_tweet_type_language_reply</th>\n",
       "      <th>TE_links_media_tweet_type_language_reply</th>\n",
       "      <th>TE_hashtags_media_tweet_type_language_reply</th>\n",
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       "      <td>0.008396</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.028447</td>\n",
       "      <td>0.033600</td>\n",
       "      <td>0.034768</td>\n",
       "      <td>0.033600</td>\n",
       "      <td>0.007015</td>\n",
       "      <td>0.007316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.066342</td>\n",
       "      <td>0.054597</td>\n",
       "      <td>0.051955</td>\n",
       "      <td>0.039584</td>\n",
       "      <td>0.054543</td>\n",
       "      <td>0.037854</td>\n",
       "      <td>0.049571</td>\n",
       "      <td>0.052935</td>\n",
       "      <td>0.047789</td>\n",
       "      <td>0.047789</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146256003</th>\n",
       "      <td>6845</td>\n",
       "      <td>73443138</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>1581799075</td>\n",
       "      <td>6937005</td>\n",
       "      <td>4354</td>\n",
       "      <td>3629</td>\n",
       "      <td>0</td>\n",
       "      <td>1269050894</td>\n",
       "      <td>3141261</td>\n",
       "      <td>4354</td>\n",
       "      <td>3629</td>\n",
       "      <td>0</td>\n",
       "      <td>1501554925</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>146256003</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>216</td>\n",
       "      <td>71</td>\n",
       "      <td>50323633</td>\n",
       "      <td>44880037</td>\n",
       "      <td>2674107</td>\n",
       "      <td>2769099</td>\n",
       "      <td>646</td>\n",
       "      <td>2378</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.008396</td>\n",
       "      <td>0.017660</td>\n",
       "      <td>0.042529</td>\n",
       "      <td>0.033600</td>\n",
       "      <td>0.034768</td>\n",
       "      <td>0.033600</td>\n",
       "      <td>0.020485</td>\n",
       "      <td>0.007316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.043432</td>\n",
       "      <td>0.054597</td>\n",
       "      <td>0.030360</td>\n",
       "      <td>0.025798</td>\n",
       "      <td>0.054543</td>\n",
       "      <td>0.025358</td>\n",
       "      <td>0.029912</td>\n",
       "      <td>0.052935</td>\n",
       "      <td>0.030454</td>\n",
       "      <td>0.030454</td>\n",
       "      <td>0.014442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hashtags  tweet_id  media  links  domains  tweet_type  language  \\\n",
       "146255999         0  73443134      0      0        0           1        59   \n",
       "146256000         0  73443135      0      0        0           1        54   \n",
       "146256001         0  73443136      9      0        0           1         4   \n",
       "146256002    845560  73443137      0      0        0           2        11   \n",
       "146256003      6845  73443138      7      0        0           2        11   \n",
       "\n",
       "            timestamp  a_user_id  a_follower_count  a_following_count  \\\n",
       "146255999  1582081341    1362744              5390                833   \n",
       "146256000  1582091134     179475             17747               1467   \n",
       "146256001  1582086464     366281              4386                 80   \n",
       "146256002  1581665518    1030299              4236               4119   \n",
       "146256003  1581799075    6937005              4354               3629   \n",
       "\n",
       "           a_is_verified  a_account_creation  b_user_id  b_follower_count  \\\n",
       "146255999              0          1546658592   30848700              5390   \n",
       "146256000              0          1172558525    4950585             17747   \n",
       "146256001              0          1457176980   30312854              4386   \n",
       "146256002              0          1524226898    3141261              4354   \n",
       "146256003              0          1269050894    3141261              4354   \n",
       "\n",
       "           b_following_count  b_is_verified  b_account_creation  b_follows_a  \\\n",
       "146255999                833              0          1428007228            0   \n",
       "146256000               1467              0          1272381192            0   \n",
       "146256001                 80              0          1235182992            0   \n",
       "146256002               3629              0          1501554925            1   \n",
       "146256003               3629              0          1501554925            1   \n",
       "\n",
       "           reply  retweet  retweet_comment  like         id  tr  dt_day  \\\n",
       "146255999      0        0                0     0  146255999   2      19   \n",
       "146256000      0        0                0     0  146256000   2      19   \n",
       "146256001      0        0                0     0  146256001   2      19   \n",
       "146256002      0        0                0     0  146256002   2      14   \n",
       "146256003      0        0                0     0  146256003   2      15   \n",
       "\n",
       "           dt_dow  dt_hour  a_count_combined  a_user_fer_count_delta_time  \\\n",
       "146255999       2        3                 2                            1   \n",
       "146256000       2        5                 0                            0   \n",
       "146256001       2        4                 2                            1   \n",
       "146256002       4        7                 2                            1   \n",
       "146256003       5       20                 2                            1   \n",
       "\n",
       "           a_user_fing_count_delta_time  a_user_fering_count_delta_time  \\\n",
       "146255999                             1                               1   \n",
       "146256000                             1                               0   \n",
       "146256001                             1                               1   \n",
       "146256002                             1                               1   \n",
       "146256003                             1                               1   \n",
       "\n",
       "           a_user_fing_count_mode  a_user_fer_count_mode  \\\n",
       "146255999                       1                      1   \n",
       "146256000                       1                      0   \n",
       "146256001                       1                      1   \n",
       "146256002                       1                      1   \n",
       "146256003                       1                      1   \n",
       "\n",
       "           a_user_fering_count_mode  count_ats  count_char  count_words  \\\n",
       "146255999                         1          0          76           15   \n",
       "146256000                         0          0         159           29   \n",
       "146256001                         1          0          72           10   \n",
       "146256002                         1          0         161           42   \n",
       "146256003                         1          0         216           71   \n",
       "\n",
       "            tw_hash  tw_freq_hash  tw_first_word  tw_second_word  \\\n",
       "146255999  39170347      34858164          28651          210319   \n",
       "146256000  38847520       1535005         300662          267542   \n",
       "146256001  49748666      44369960        7361311          466019   \n",
       "146256002  50323632      44880036        7551233          985413   \n",
       "146256003  50323633      44880037        2674107         2769099   \n",
       "\n",
       "           tw_last_word  tw_llast_word  tw_len  tw_hash0  tw_hash1  \\\n",
       "146255999          2793          27536       6         0         0   \n",
       "146256000            11            867      13         0         0   \n",
       "146256001         19261         202536       5         0         0   \n",
       "146256002           112           2712      14         0         0   \n",
       "146256003           646           2378      26         0         0   \n",
       "\n",
       "           tw_rt_uhash  TE_b_user_id_tweet_type_language_reply  \\\n",
       "146255999         7739                                     NaN   \n",
       "146256000        10748                                0.023279   \n",
       "146256001        58462                                     NaN   \n",
       "146256002            0                                0.008396   \n",
       "146256003            0                                0.008396   \n",
       "\n",
       "           TE_tw_first_word_tweet_type_language_reply  \\\n",
       "146255999                                    0.006108   \n",
       "146256000                                    0.010420   \n",
       "146256001                                    0.024387   \n",
       "146256002                                         NaN   \n",
       "146256003                                    0.017660   \n",
       "\n",
       "           TE_tw_last_word_tweet_type_language_reply  \\\n",
       "146255999                                   0.005729   \n",
       "146256000                                   0.009034   \n",
       "146256001                                   0.005437   \n",
       "146256002                                   0.028447   \n",
       "146256003                                   0.042529   \n",
       "\n",
       "           TE_tw_hash0_tweet_type_language_reply  \\\n",
       "146255999                               0.006408   \n",
       "146256000                               0.008012   \n",
       "146256001                               0.003214   \n",
       "146256002                               0.033600   \n",
       "146256003                               0.033600   \n",
       "\n",
       "           TE_tw_hash1_tweet_type_language_reply  \\\n",
       "146255999                               0.006432   \n",
       "146256000                               0.007989   \n",
       "146256001                               0.003181   \n",
       "146256002                               0.034768   \n",
       "146256003                               0.034768   \n",
       "\n",
       "           TE_tw_rt_uhash_tweet_type_language_reply  TE_a_user_id_reply  \\\n",
       "146255999                                  0.005683            0.009484   \n",
       "146256000                                  0.012797            0.030540   \n",
       "146256001                                  0.008218            0.016115   \n",
       "146256002                                  0.033600            0.007015   \n",
       "146256003                                  0.033600            0.020485   \n",
       "\n",
       "           TE_b_user_id_reply  TE_tw_hash_reply  TE_tw_freq_hash_reply  \\\n",
       "146255999                 NaN               NaN                    NaN   \n",
       "146256000            0.017071               NaN               0.024387   \n",
       "146256001                 NaN               NaN                    NaN   \n",
       "146256002            0.007316               NaN                    NaN   \n",
       "146256003            0.007316               NaN                    NaN   \n",
       "\n",
       "           TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_reply  \\\n",
       "146255999                                           0.006223                            \n",
       "146256000                                           0.008186                            \n",
       "146256001                                           0.001729                            \n",
       "146256002                                           0.066342                            \n",
       "146256003                                           0.043432                            \n",
       "\n",
       "           TE_a_count_combined_tweet_type_language_reply  \\\n",
       "146255999                                       0.015375   \n",
       "146256000                                       0.000038   \n",
       "146256001                                       0.006054   \n",
       "146256002                                       0.054597   \n",
       "146256003                                       0.054597   \n",
       "\n",
       "           TE_a_user_fer_count_delta_time_media_language_reply  \\\n",
       "146255999                                           0.058077     \n",
       "146256000                                           0.000565     \n",
       "146256001                                           0.006283     \n",
       "146256002                                           0.051955     \n",
       "146256003                                           0.030360     \n",
       "\n",
       "           TE_a_user_fing_count_delta_time_media_language_reply  \\\n",
       "146255999                                           0.049670      \n",
       "146256000                                           0.037648      \n",
       "146256001                                           0.005231      \n",
       "146256002                                           0.039584      \n",
       "146256003                                           0.025798      \n",
       "\n",
       "           TE_a_user_fering_count_delta_time_tweet_type_language_reply  \\\n",
       "146255999                                           0.015181             \n",
       "146256000                                           0.000094             \n",
       "146256001                                           0.006042             \n",
       "146256002                                           0.054543             \n",
       "146256003                                           0.054543             \n",
       "\n",
       "           TE_a_user_fing_count_mode_media_language_reply  \\\n",
       "146255999                                        0.047975   \n",
       "146256000                                        0.035468   \n",
       "146256001                                        0.005186   \n",
       "146256002                                        0.037854   \n",
       "146256003                                        0.025358   \n",
       "\n",
       "           TE_a_user_fer_count_mode_media_language_reply  \\\n",
       "146255999                                       0.055960   \n",
       "146256000                                       0.001059   \n",
       "146256001                                       0.006187   \n",
       "146256002                                       0.049571   \n",
       "146256003                                       0.029912   \n",
       "\n",
       "           TE_a_user_fering_count_mode_tweet_type_language_reply  \\\n",
       "146255999                                           0.013968       \n",
       "146256000                                           0.000372       \n",
       "146256001                                           0.005747       \n",
       "146256002                                           0.052935       \n",
       "146256003                                           0.052935       \n",
       "\n",
       "           TE_domains_media_tweet_type_language_reply  \\\n",
       "146255999                                    0.006116   \n",
       "146256000                                    0.008544   \n",
       "146256001                                    0.001734   \n",
       "146256002                                    0.047789   \n",
       "146256003                                    0.030454   \n",
       "\n",
       "           TE_links_media_tweet_type_language_reply  \\\n",
       "146255999                                  0.006116   \n",
       "146256000                                  0.008544   \n",
       "146256001                                  0.001734   \n",
       "146256002                                  0.047789   \n",
       "146256003                                  0.030454   \n",
       "\n",
       "           TE_hashtags_media_tweet_type_language_reply  \n",
       "146255999                                     0.006173  \n",
       "146256000                                     0.008717  \n",
       "146256001                                     0.001787  \n",
       "146256002                                          NaN  \n",
       "146256003                                     0.014442  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hashtags                                                                        int32\n",
       "tweet_id                                                                        int32\n",
       "media                                                                            int8\n",
       "links                                                                           int32\n",
       "domains                                                                         int32\n",
       "tweet_type                                                                       int8\n",
       "language                                                                         int8\n",
       "timestamp                                                                       int32\n",
       "a_user_id                                                                       int32\n",
       "a_follower_count                                                                int32\n",
       "a_following_count                                                               int32\n",
       "a_is_verified                                                                    int8\n",
       "a_account_creation                                                              int32\n",
       "b_user_id                                                                       int32\n",
       "b_follower_count                                                                int32\n",
       "b_following_count                                                               int32\n",
       "b_is_verified                                                                    int8\n",
       "b_account_creation                                                              int32\n",
       "b_follows_a                                                                      int8\n",
       "reply                                                                           int32\n",
       "retweet                                                                         int32\n",
       "retweet_comment                                                                 int32\n",
       "like                                                                            int32\n",
       "id                                                                              int32\n",
       "tr                                                                              int32\n",
       "dt_day                                                                           int8\n",
       "dt_dow                                                                           int8\n",
       "dt_hour                                                                          int8\n",
       "a_count_combined                                                                int32\n",
       "a_user_fer_count_delta_time                                                     int32\n",
       "a_user_fing_count_delta_time                                                    int32\n",
       "a_user_fering_count_delta_time                                                  int32\n",
       "a_user_fing_count_mode                                                          int32\n",
       "a_user_fer_count_mode                                                           int32\n",
       "a_user_fering_count_mode                                                        int32\n",
       "count_ats                                                                       int32\n",
       "count_char                                                                      int32\n",
       "count_words                                                                     int32\n",
       "tw_hash                                                                         int32\n",
       "tw_freq_hash                                                                    int32\n",
       "tw_first_word                                                                   int32\n",
       "tw_second_word                                                                  int32\n",
       "tw_last_word                                                                    int32\n",
       "tw_llast_word                                                                   int32\n",
       "tw_len                                                                          int32\n",
       "tw_hash0                                                                        int32\n",
       "tw_hash1                                                                        int32\n",
       "tw_rt_uhash                                                                     int32\n",
       "TE_b_user_id_tweet_type_language_reply                                        float32\n",
       "TE_tw_first_word_tweet_type_language_reply                                    float32\n",
       "TE_tw_last_word_tweet_type_language_reply                                     float32\n",
       "TE_tw_hash0_tweet_type_language_reply                                         float32\n",
       "TE_tw_hash1_tweet_type_language_reply                                         float32\n",
       "TE_tw_rt_uhash_tweet_type_language_reply                                      float32\n",
       "TE_a_user_id_reply                                                            float32\n",
       "TE_b_user_id_reply                                                            float32\n",
       "TE_tw_hash_reply                                                              float32\n",
       "TE_tw_freq_hash_reply                                                         float32\n",
       "TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_reply    float32\n",
       "TE_a_count_combined_tweet_type_language_reply                                 float32\n",
       "TE_a_user_fer_count_delta_time_media_language_reply                           float32\n",
       "TE_a_user_fing_count_delta_time_media_language_reply                          float32\n",
       "TE_a_user_fering_count_delta_time_tweet_type_language_reply                   float32\n",
       "TE_a_user_fing_count_mode_media_language_reply                                float32\n",
       "TE_a_user_fer_count_mode_media_language_reply                                 float32\n",
       "TE_a_user_fering_count_mode_tweet_type_language_reply                         float32\n",
       "TE_domains_media_tweet_type_language_reply                                    float32\n",
       "TE_links_media_tweet_type_language_reply                                      float32\n",
       "TE_hashtags_media_tweet_type_language_reply                                   float32\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "save_memory( train )\n",
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using 35 features:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['media', 'tweet_type', 'a_follower_count', 'a_following_count',\n",
       "       'a_is_verified', 'b_follower_count', 'b_following_count',\n",
       "       'b_follows_a', 'dt_dow', 'dt_hour', 'count_ats', 'count_char',\n",
       "       'count_words', 'tw_len', 'TE_b_user_id_tweet_type_language_reply',\n",
       "       'TE_tw_first_word_tweet_type_language_reply',\n",
       "       'TE_tw_last_word_tweet_type_language_reply',\n",
       "       'TE_tw_hash0_tweet_type_language_reply',\n",
       "       'TE_tw_hash1_tweet_type_language_reply',\n",
       "       'TE_tw_rt_uhash_tweet_type_language_reply', 'TE_a_user_id_reply',\n",
       "       'TE_b_user_id_reply', 'TE_tw_hash_reply', 'TE_tw_freq_hash_reply',\n",
       "       'TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_reply',\n",
       "       'TE_a_count_combined_tweet_type_language_reply',\n",
       "       'TE_a_user_fer_count_delta_time_media_language_reply',\n",
       "       'TE_a_user_fing_count_delta_time_media_language_reply',\n",
       "       'TE_a_user_fering_count_delta_time_tweet_type_language_reply',\n",
       "       'TE_a_user_fing_count_mode_media_language_reply',\n",
       "       'TE_a_user_fer_count_mode_media_language_reply',\n",
       "       'TE_a_user_fering_count_mode_tweet_type_language_reply',\n",
       "       'TE_domains_media_tweet_type_language_reply',\n",
       "       'TE_links_media_tweet_type_language_reply',\n",
       "       'TE_hashtags_media_tweet_type_language_reply'], dtype='<U74')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_names = ['reply', 'retweet', 'retweet_comment', 'like']\n",
    "DONT_USE = ['timestamp','a_account_creation','b_account_creation','engage_time',\n",
    "            'a_account_creation', 'b_account_creation',\n",
    "            'fold','tweet_id', \n",
    "            'tr','dt_day','','',\n",
    "            'b_user_id','a_user_id','b_is_verified',\n",
    "            'elapsed_time',\n",
    "            'links','domains','hashtags0','hashtags1',\n",
    "            'hashtags','tweet_hash','dt_second','id',\n",
    "            'tw_hash0',\n",
    "            'tw_hash1',\n",
    "            'tw_rt_uhash',\n",
    "            'same_language', 'nan_language','language',\n",
    "            'tw_hash', 'tw_freq_hash','tw_first_word', 'tw_second_word', 'tw_last_word', 'tw_llast_word',\n",
    "            'ypred','a_count_combined','a_user_fer_count_delta_time','a_user_fing_count_delta_time','a_user_fering_count_delta_time','a_user_fing_count_mode','a_user_fer_count_mode','a_user_fering_count_mode'\n",
    "            \n",
    "           ]\n",
    "DONT_USE += label_names\n",
    "features = [c for c in train.columns if c not in DONT_USE]\n",
    "\n",
    "print('Using %i features:'%(len(features)))\n",
    "np.asarray(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hashtags</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>media</th>\n",
       "      <th>links</th>\n",
       "      <th>domains</th>\n",
       "      <th>tweet_type</th>\n",
       "      <th>language</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>a_user_id</th>\n",
       "      <th>a_follower_count</th>\n",
       "      <th>a_following_count</th>\n",
       "      <th>a_is_verified</th>\n",
       "      <th>a_account_creation</th>\n",
       "      <th>b_user_id</th>\n",
       "      <th>b_follower_count</th>\n",
       "      <th>b_following_count</th>\n",
       "      <th>b_is_verified</th>\n",
       "      <th>b_account_creation</th>\n",
       "      <th>b_follows_a</th>\n",
       "      <th>reply</th>\n",
       "      <th>retweet</th>\n",
       "      <th>retweet_comment</th>\n",
       "      <th>like</th>\n",
       "      <th>id</th>\n",
       "      <th>tr</th>\n",
       "      <th>dt_day</th>\n",
       "      <th>dt_dow</th>\n",
       "      <th>dt_hour</th>\n",
       "      <th>a_count_combined</th>\n",
       "      <th>a_user_fer_count_delta_time</th>\n",
       "      <th>a_user_fing_count_delta_time</th>\n",
       "      <th>a_user_fering_count_delta_time</th>\n",
       "      <th>a_user_fing_count_mode</th>\n",
       "      <th>a_user_fer_count_mode</th>\n",
       "      <th>a_user_fering_count_mode</th>\n",
       "      <th>count_ats</th>\n",
       "      <th>count_char</th>\n",
       "      <th>count_words</th>\n",
       "      <th>tw_hash</th>\n",
       "      <th>tw_freq_hash</th>\n",
       "      <th>tw_first_word</th>\n",
       "      <th>tw_second_word</th>\n",
       "      <th>tw_last_word</th>\n",
       "      <th>tw_llast_word</th>\n",
       "      <th>tw_len</th>\n",
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       "      <th>tw_hash1</th>\n",
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       "      <th>TE_b_user_id_tweet_type_language_reply</th>\n",
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       "      <th>TE_tw_last_word_tweet_type_language_reply</th>\n",
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       "      <th>TE_a_count_combined_tweet_type_language_reply</th>\n",
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       "      <th>TE_a_user_fing_count_delta_time_media_language_reply</th>\n",
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       "      <th>TE_a_user_fing_count_mode_media_language_reply</th>\n",
       "      <th>TE_a_user_fer_count_mode_media_language_reply</th>\n",
       "      <th>TE_a_user_fering_count_mode_tweet_type_language_reply</th>\n",
       "      <th>TE_domains_media_tweet_type_language_reply</th>\n",
       "      <th>TE_links_media_tweet_type_language_reply</th>\n",
       "      <th>TE_hashtags_media_tweet_type_language_reply</th>\n",
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       "</div>"
      ],
      "text/plain": [
       "           hashtags  tweet_id  media  links  domains  tweet_type  language  \\\n",
       "146255999         0  73443134      0      0        0           1        59   \n",
       "146256000         0  73443135      0      0        0           1        54   \n",
       "146256001         0  73443136      9      0        0           1         4   \n",
       "146256002    845560  73443137      0      0        0           2        11   \n",
       "146256003      6845  73443138      7      0        0           2        11   \n",
       "\n",
       "            timestamp  a_user_id  a_follower_count  a_following_count  \\\n",
       "146255999  1582081341    1362744              5390                833   \n",
       "146256000  1582091134     179475             17747               1467   \n",
       "146256001  1582086464     366281              4386                 80   \n",
       "146256002  1581665518    1030299              4236               4119   \n",
       "146256003  1581799075    6937005              4354               3629   \n",
       "\n",
       "           a_is_verified  a_account_creation  b_user_id  b_follower_count  \\\n",
       "146255999              0          1546658592   30848700              5390   \n",
       "146256000              0          1172558525    4950585             17747   \n",
       "146256001              0          1457176980   30312854              4386   \n",
       "146256002              0          1524226898    3141261              4354   \n",
       "146256003              0          1269050894    3141261              4354   \n",
       "\n",
       "           b_following_count  b_is_verified  b_account_creation  b_follows_a  \\\n",
       "146255999                833              0          1428007228            0   \n",
       "146256000               1467              0          1272381192            0   \n",
       "146256001                 80              0          1235182992            0   \n",
       "146256002               3629              0          1501554925            1   \n",
       "146256003               3629              0          1501554925            1   \n",
       "\n",
       "           reply  retweet  retweet_comment  like         id  tr  dt_day  \\\n",
       "146255999      0        0                0     0  146255999   2      19   \n",
       "146256000      0        0                0     0  146256000   2      19   \n",
       "146256001      0        0                0     0  146256001   2      19   \n",
       "146256002      0        0                0     0  146256002   2      14   \n",
       "146256003      0        0                0     0  146256003   2      15   \n",
       "\n",
       "           dt_dow  dt_hour  a_count_combined  a_user_fer_count_delta_time  \\\n",
       "146255999       2        3                 2                            1   \n",
       "146256000       2        5                 0                            0   \n",
       "146256001       2        4                 2                            1   \n",
       "146256002       4        7                 2                            1   \n",
       "146256003       5       20                 2                            1   \n",
       "\n",
       "           a_user_fing_count_delta_time  a_user_fering_count_delta_time  \\\n",
       "146255999                             1                               1   \n",
       "146256000                             1                               0   \n",
       "146256001                             1                               1   \n",
       "146256002                             1                               1   \n",
       "146256003                             1                               1   \n",
       "\n",
       "           a_user_fing_count_mode  a_user_fer_count_mode  \\\n",
       "146255999                       1                      1   \n",
       "146256000                       1                      0   \n",
       "146256001                       1                      1   \n",
       "146256002                       1                      1   \n",
       "146256003                       1                      1   \n",
       "\n",
       "           a_user_fering_count_mode  count_ats  count_char  count_words  \\\n",
       "146255999                         1          0          76           15   \n",
       "146256000                         0          0         159           29   \n",
       "146256001                         1          0          72           10   \n",
       "146256002                         1          0         161           42   \n",
       "146256003                         1          0         216           71   \n",
       "\n",
       "            tw_hash  tw_freq_hash  tw_first_word  tw_second_word  \\\n",
       "146255999  39170347      34858164          28651          210319   \n",
       "146256000  38847520       1535005         300662          267542   \n",
       "146256001  49748666      44369960        7361311          466019   \n",
       "146256002  50323632      44880036        7551233          985413   \n",
       "146256003  50323633      44880037        2674107         2769099   \n",
       "\n",
       "           tw_last_word  tw_llast_word  tw_len  tw_hash0  tw_hash1  \\\n",
       "146255999          2793          27536       6         0         0   \n",
       "146256000            11            867      13         0         0   \n",
       "146256001         19261         202536       5         0         0   \n",
       "146256002           112           2712      14         0         0   \n",
       "146256003           646           2378      26         0         0   \n",
       "\n",
       "           tw_rt_uhash  TE_b_user_id_tweet_type_language_reply  \\\n",
       "146255999         7739                                     NaN   \n",
       "146256000        10748                                0.023279   \n",
       "146256001        58462                                     NaN   \n",
       "146256002            0                                0.008396   \n",
       "146256003            0                                0.008396   \n",
       "\n",
       "           TE_tw_first_word_tweet_type_language_reply  \\\n",
       "146255999                                    0.006108   \n",
       "146256000                                    0.010420   \n",
       "146256001                                    0.024387   \n",
       "146256002                                         NaN   \n",
       "146256003                                    0.017660   \n",
       "\n",
       "           TE_tw_last_word_tweet_type_language_reply  \\\n",
       "146255999                                   0.005729   \n",
       "146256000                                   0.009034   \n",
       "146256001                                   0.005437   \n",
       "146256002                                   0.028447   \n",
       "146256003                                   0.042529   \n",
       "\n",
       "           TE_tw_hash0_tweet_type_language_reply  \\\n",
       "146255999                               0.006408   \n",
       "146256000                               0.008012   \n",
       "146256001                               0.003214   \n",
       "146256002                               0.033600   \n",
       "146256003                               0.033600   \n",
       "\n",
       "           TE_tw_hash1_tweet_type_language_reply  \\\n",
       "146255999                               0.006432   \n",
       "146256000                               0.007989   \n",
       "146256001                               0.003181   \n",
       "146256002                               0.034768   \n",
       "146256003                               0.034768   \n",
       "\n",
       "           TE_tw_rt_uhash_tweet_type_language_reply  TE_a_user_id_reply  \\\n",
       "146255999                                  0.005683            0.009484   \n",
       "146256000                                  0.012797            0.030540   \n",
       "146256001                                  0.008218            0.016115   \n",
       "146256002                                  0.033600            0.007015   \n",
       "146256003                                  0.033600            0.020485   \n",
       "\n",
       "           TE_b_user_id_reply  TE_tw_hash_reply  TE_tw_freq_hash_reply  \\\n",
       "146255999                 NaN               NaN                    NaN   \n",
       "146256000            0.017071               NaN               0.024387   \n",
       "146256001                 NaN               NaN                    NaN   \n",
       "146256002            0.007316               NaN                    NaN   \n",
       "146256003            0.007316               NaN                    NaN   \n",
       "\n",
       "           TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_reply  \\\n",
       "146255999                                           0.006223                            \n",
       "146256000                                           0.008186                            \n",
       "146256001                                           0.001729                            \n",
       "146256002                                           0.066342                            \n",
       "146256003                                           0.043432                            \n",
       "\n",
       "           TE_a_count_combined_tweet_type_language_reply  \\\n",
       "146255999                                       0.015375   \n",
       "146256000                                       0.000038   \n",
       "146256001                                       0.006054   \n",
       "146256002                                       0.054597   \n",
       "146256003                                       0.054597   \n",
       "\n",
       "           TE_a_user_fer_count_delta_time_media_language_reply  \\\n",
       "146255999                                           0.058077     \n",
       "146256000                                           0.000565     \n",
       "146256001                                           0.006283     \n",
       "146256002                                           0.051955     \n",
       "146256003                                           0.030360     \n",
       "\n",
       "           TE_a_user_fing_count_delta_time_media_language_reply  \\\n",
       "146255999                                           0.049670      \n",
       "146256000                                           0.037648      \n",
       "146256001                                           0.005231      \n",
       "146256002                                           0.039584      \n",
       "146256003                                           0.025798      \n",
       "\n",
       "           TE_a_user_fering_count_delta_time_tweet_type_language_reply  \\\n",
       "146255999                                           0.015181             \n",
       "146256000                                           0.000094             \n",
       "146256001                                           0.006042             \n",
       "146256002                                           0.054543             \n",
       "146256003                                           0.054543             \n",
       "\n",
       "           TE_a_user_fing_count_mode_media_language_reply  \\\n",
       "146255999                                        0.047975   \n",
       "146256000                                        0.035468   \n",
       "146256001                                        0.005186   \n",
       "146256002                                        0.037854   \n",
       "146256003                                        0.025358   \n",
       "\n",
       "           TE_a_user_fer_count_mode_media_language_reply  \\\n",
       "146255999                                       0.055960   \n",
       "146256000                                       0.001059   \n",
       "146256001                                       0.006187   \n",
       "146256002                                       0.049571   \n",
       "146256003                                       0.029912   \n",
       "\n",
       "           TE_a_user_fering_count_mode_tweet_type_language_reply  \\\n",
       "146255999                                           0.013968       \n",
       "146256000                                           0.000372       \n",
       "146256001                                           0.005747       \n",
       "146256002                                           0.052935       \n",
       "146256003                                           0.052935       \n",
       "\n",
       "           TE_domains_media_tweet_type_language_reply  \\\n",
       "146255999                                    0.006116   \n",
       "146256000                                    0.008544   \n",
       "146256001                                    0.001734   \n",
       "146256002                                    0.047789   \n",
       "146256003                                    0.030454   \n",
       "\n",
       "           TE_links_media_tweet_type_language_reply  \\\n",
       "146255999                                  0.006116   \n",
       "146256000                                  0.008544   \n",
       "146256001                                  0.001734   \n",
       "146256002                                  0.047789   \n",
       "146256003                                  0.030454   \n",
       "\n",
       "           TE_hashtags_media_tweet_type_language_reply  \n",
       "146255999                                     0.006173  \n",
       "146256000                                     0.008717  \n",
       "146256001                                     0.001787  \n",
       "146256002                                          NaN  \n",
       "146256003                                     0.014442  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60714530, 69), (60671901, 69), (12434735, 69), (12434838, 69))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(2020)\n",
    "SEED = 1\n",
    "\n",
    "valid = train.loc[ ((train.tweet_id % 2)==1)&(train.tr==0) ]\n",
    "gc.collect()\n",
    "\n",
    "test0 = train.loc[ (train.tr==1) ]\n",
    "gc.collect()\n",
    "\n",
    "test1 = train.loc[ (train.tr==2) ]\n",
    "gc.collect()\n",
    "\n",
    "train = train.loc[ ((train.tweet_id % 2)==0)&(train.tr==0) ]\n",
    "gc.collect()\n",
    "\n",
    "train.shape, valid.shape, test0.shape, test1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGB Version 1.1.0\n"
     ]
    }
   ],
   "source": [
    "xgb_parms = { \n",
    "    'max_depth':8, \n",
    "    'learning_rate':0.025, \n",
    "    'subsample':0.85,\n",
    "    'colsample_bytree':0.35, \n",
    "    'eval_metric':'logloss',\n",
    "    'objective':'binary:logistic',\n",
    "    'tree_method':'gpu_hist',\n",
    "    #'predictor': 'gpu_predictor',\n",
    "    'seed': 1,\n",
    "}\n",
    "\n",
    "import xgboost as xgb\n",
    "print('XGB Version',xgb.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CREATE TRAIN AND VALIDATION SETS\n",
    "RMV = [c for c in DONT_USE if c in train.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#########################\n",
      "### reply\n",
      "#########################\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#LEarning rates for 'reply', 'retweet', 'retweet_comment', 'like'\n",
    "LR = [0.05,0.03,0.07,0.01]\n",
    "\n",
    "#Like\n",
    "xgb_parms['learning_rate'] = LR[TARGET_id]\n",
    "\n",
    "print('#'*25);print('###',TARGET);print('#'*25)\n",
    "\n",
    "dtrain = xgb.DMatrix(data=train.drop(RMV, axis=1) ,label=train[TARGET].values)\n",
    "gc.collect()\n",
    "\n",
    "model = xgb.train(xgb_parms, \n",
    "                  dtrain=dtrain,\n",
    "                  num_boost_round=500,\n",
    "                 ) \n",
    "\n",
    "del dtrain\n",
    "gc.collect()  \n",
    "\n",
    "#save model\n",
    "joblib.dump(model, 'model-'+TARGET+'-1.xgb' ) \n",
    "del model\n",
    "gc.collect()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtrain = xgb.DMatrix(data=valid.drop(RMV, axis=1) ,label=valid[TARGET].values)\n",
    "gc.collect()\n",
    "\n",
    "model = xgb.train(xgb_parms, \n",
    "                  dtrain=dtrain,\n",
    "                  num_boost_round=500,\n",
    "                 ) \n",
    "\n",
    "del dtrain\n",
    "gc.collect()  \n",
    "\n",
    "#save model\n",
    "joblib.dump(model, 'model-'+TARGET+'-2.xgb' ) \n",
    "\n",
    "del model\n",
    "gc.collect()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load xgb1\n"
     ]
    }
   ],
   "source": [
    "print('load xgb1')\n",
    "model = joblib.load( 'model-'+TARGET+'-1.xgb' )\n",
    "dvalid = xgb.DMatrix(data=valid.drop(RMV, axis=1))\n",
    "valid['ypred'] = model.predict(dvalid)\n",
    "del dvalid, model; _=gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load xgb2\n"
     ]
    }
   ],
   "source": [
    "print('load xgb2')\n",
    "model = joblib.load( 'model-'+TARGET+'-2.xgb' )\n",
    "dtrain = xgb.DMatrix(data=train.drop(RMV, axis=1))\n",
    "train['ypred'] = model.predict(dtrain)\n",
    "del dtrain, model; _=gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OOF RCE:\n",
      "24.565024482645846\n",
      "24.576664209086363\n"
     ]
    }
   ],
   "source": [
    "print(\"OOF RCE:\")\n",
    "print( compute_rce( valid[TARGET].values, valid['ypred'].values ) )\n",
    "print( compute_rce( train[TARGET].values, train['ypred'].values ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7eddb1cc24a8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt0 = train.groupby( ['dt_day','dt_hour'] )['ypred'].agg('mean').reset_index()\n",
    "dt1 = valid.groupby( ['dt_day','dt_hour'] )['ypred'].agg('mean').reset_index()\n",
    "dt0.sort_values(['dt_day','dt_hour'], inplace=True)\n",
    "dt1.sort_values(['dt_day','dt_hour'], inplace=True)\n",
    "\n",
    "dt0['ypred2'] = dt1['ypred'].values\n",
    "\n",
    "dt0[['ypred','ypred2']].plot(  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7eddb1cba518>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xgboost import plot_importance\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"figure.figsize\"] = (12,12)\n",
    "\n",
    "model = joblib.load( 'model-'+TARGET+'-1.xgb' )\n",
    "plot_importance(model, height=1.0, show_values=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Public val.tsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "test0.sort_values( 'id', inplace=True )\n",
    "sub = pd.read_csv('../preprocessings/sample_submission_public.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hashtags</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>media</th>\n",
       "      <th>links</th>\n",
       "      <th>domains</th>\n",
       "      <th>tweet_type</th>\n",
       "      <th>language</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>a_user_id</th>\n",
       "      <th>a_follower_count</th>\n",
       "      <th>a_following_count</th>\n",
       "      <th>a_is_verified</th>\n",
       "      <th>a_account_creation</th>\n",
       "      <th>b_user_id</th>\n",
       "      <th>b_follower_count</th>\n",
       "      <th>b_following_count</th>\n",
       "      <th>b_is_verified</th>\n",
       "      <th>b_account_creation</th>\n",
       "      <th>b_follows_a</th>\n",
       "      <th>reply</th>\n",
       "      <th>retweet</th>\n",
       "      <th>retweet_comment</th>\n",
       "      <th>like</th>\n",
       "      <th>id</th>\n",
       "      <th>tr</th>\n",
       "      <th>dt_day</th>\n",
       "      <th>dt_dow</th>\n",
       "      <th>dt_hour</th>\n",
       "      <th>a_count_combined</th>\n",
       "      <th>a_user_fer_count_delta_time</th>\n",
       "      <th>a_user_fing_count_delta_time</th>\n",
       "      <th>a_user_fering_count_delta_time</th>\n",
       "      <th>a_user_fing_count_mode</th>\n",
       "      <th>a_user_fer_count_mode</th>\n",
       "      <th>a_user_fering_count_mode</th>\n",
       "      <th>count_ats</th>\n",
       "      <th>count_char</th>\n",
       "      <th>count_words</th>\n",
       "      <th>tw_hash</th>\n",
       "      <th>tw_freq_hash</th>\n",
       "      <th>tw_first_word</th>\n",
       "      <th>tw_second_word</th>\n",
       "      <th>tw_last_word</th>\n",
       "      <th>tw_llast_word</th>\n",
       "      <th>tw_len</th>\n",
       "      <th>tw_hash0</th>\n",
       "      <th>tw_hash1</th>\n",
       "      <th>tw_rt_uhash</th>\n",
       "      <th>TE_b_user_id_tweet_type_language_reply</th>\n",
       "      <th>TE_tw_first_word_tweet_type_language_reply</th>\n",
       "      <th>TE_tw_last_word_tweet_type_language_reply</th>\n",
       "      <th>TE_tw_hash0_tweet_type_language_reply</th>\n",
       "      <th>TE_tw_hash1_tweet_type_language_reply</th>\n",
       "      <th>TE_tw_rt_uhash_tweet_type_language_reply</th>\n",
       "      <th>TE_a_user_id_reply</th>\n",
       "      <th>TE_b_user_id_reply</th>\n",
       "      <th>TE_tw_hash_reply</th>\n",
       "      <th>TE_tw_freq_hash_reply</th>\n",
       "      <th>TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_reply</th>\n",
       "      <th>TE_a_count_combined_tweet_type_language_reply</th>\n",
       "      <th>TE_a_user_fer_count_delta_time_media_language_reply</th>\n",
       "      <th>TE_a_user_fing_count_delta_time_media_language_reply</th>\n",
       "      <th>TE_a_user_fering_count_delta_time_tweet_type_language_reply</th>\n",
       "      <th>TE_a_user_fing_count_mode_media_language_reply</th>\n",
       "      <th>TE_a_user_fer_count_mode_media_language_reply</th>\n",
       "      <th>TE_a_user_fering_count_mode_tweet_type_language_reply</th>\n",
       "      <th>TE_domains_media_tweet_type_language_reply</th>\n",
       "      <th>TE_links_media_tweet_type_language_reply</th>\n",
       "      <th>TE_hashtags_media_tweet_type_language_reply</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>121386431</th>\n",
       "      <td>0</td>\n",
       "      <td>57733249</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>54</td>\n",
       "      <td>1581703126</td>\n",
       "      <td>534117</td>\n",
       "      <td>13919</td>\n",
       "      <td>1214</td>\n",
       "      <td>0</td>\n",
       "      <td>1448292186</td>\n",
       "      <td>3617447</td>\n",
       "      <td>8794</td>\n",
       "      <td>2134</td>\n",
       "      <td>0</td>\n",
       "      <td>1520948869</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121386431</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>5</td>\n",
       "      <td>38691181</td>\n",
       "      <td>34413698</td>\n",
       "      <td>562265</td>\n",
       "      <td>296463</td>\n",
       "      <td>83986</td>\n",
       "      <td>110811</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.019697</td>\n",
       "      <td>0.020485</td>\n",
       "      <td>0.033453</td>\n",
       "      <td>0.032895</td>\n",
       "      <td>0.033455</td>\n",
       "      <td>0.007879</td>\n",
       "      <td>0.021339</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.068830</td>\n",
       "      <td>0.066553</td>\n",
       "      <td>0.042014</td>\n",
       "      <td>0.029877</td>\n",
       "      <td>0.055450</td>\n",
       "      <td>0.002924</td>\n",
       "      <td>0.040001</td>\n",
       "      <td>0.053399</td>\n",
       "      <td>0.031730</td>\n",
       "      <td>0.031730</td>\n",
       "      <td>0.029235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121386432</th>\n",
       "      <td>0</td>\n",
       "      <td>57733250</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>1582021842</td>\n",
       "      <td>2721240</td>\n",
       "      <td>186</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>1263078566</td>\n",
       "      <td>12365145</td>\n",
       "      <td>111835</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1335110299</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121386432</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>5</td>\n",
       "      <td>38691182</td>\n",
       "      <td>34413699</td>\n",
       "      <td>7376000</td>\n",
       "      <td>4871859</td>\n",
       "      <td>348771</td>\n",
       "      <td>1398132</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>102048</td>\n",
       "      <td>0.024387</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.016520</td>\n",
       "      <td>0.005397</td>\n",
       "      <td>0.005337</td>\n",
       "      <td>0.016520</td>\n",
       "      <td>0.023279</td>\n",
       "      <td>0.016004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.004327</td>\n",
       "      <td>0.005186</td>\n",
       "      <td>0.012700</td>\n",
       "      <td>0.012700</td>\n",
       "      <td>0.005186</td>\n",
       "      <td>0.012700</td>\n",
       "      <td>0.012700</td>\n",
       "      <td>0.005186</td>\n",
       "      <td>0.004740</td>\n",
       "      <td>0.004740</td>\n",
       "      <td>0.005287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121386433</th>\n",
       "      <td>0</td>\n",
       "      <td>57733251</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>1581734918</td>\n",
       "      <td>2023199</td>\n",
       "      <td>249849</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1356488269</td>\n",
       "      <td>28952089</td>\n",
       "      <td>249849</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1503940711</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121386433</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>5</td>\n",
       "      <td>38691183</td>\n",
       "      <td>18372275</td>\n",
       "      <td>16591</td>\n",
       "      <td>29975</td>\n",
       "      <td>1205</td>\n",
       "      <td>23578</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.018968</td>\n",
       "      <td>0.041869</td>\n",
       "      <td>0.042021</td>\n",
       "      <td>0.043350</td>\n",
       "      <td>0.042024</td>\n",
       "      <td>0.010243</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.024387</td>\n",
       "      <td>0.014659</td>\n",
       "      <td>0.000272</td>\n",
       "      <td>0.001125</td>\n",
       "      <td>0.038141</td>\n",
       "      <td>0.001862</td>\n",
       "      <td>0.037599</td>\n",
       "      <td>0.001141</td>\n",
       "      <td>0.002460</td>\n",
       "      <td>0.038487</td>\n",
       "      <td>0.038487</td>\n",
       "      <td>0.044228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121386434</th>\n",
       "      <td>0</td>\n",
       "      <td>57733252</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>1581913613</td>\n",
       "      <td>2816974</td>\n",
       "      <td>517</td>\n",
       "      <td>407</td>\n",
       "      <td>0</td>\n",
       "      <td>1449096567</td>\n",
       "      <td>13774342</td>\n",
       "      <td>574475</td>\n",
       "      <td>2656</td>\n",
       "      <td>0</td>\n",
       "      <td>1396311956</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121386434</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>104</td>\n",
       "      <td>16</td>\n",
       "      <td>38691184</td>\n",
       "      <td>34413700</td>\n",
       "      <td>7376001</td>\n",
       "      <td>165976</td>\n",
       "      <td>4845</td>\n",
       "      <td>16174</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2446</td>\n",
       "      <td>0.018290</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006793</td>\n",
       "      <td>0.008012</td>\n",
       "      <td>0.007989</td>\n",
       "      <td>0.001848</td>\n",
       "      <td>0.016004</td>\n",
       "      <td>0.016004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006315</td>\n",
       "      <td>0.018912</td>\n",
       "      <td>0.027443</td>\n",
       "      <td>0.023237</td>\n",
       "      <td>0.018874</td>\n",
       "      <td>0.022679</td>\n",
       "      <td>0.026802</td>\n",
       "      <td>0.017103</td>\n",
       "      <td>0.005846</td>\n",
       "      <td>0.005846</td>\n",
       "      <td>0.006122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121386435</th>\n",
       "      <td>0</td>\n",
       "      <td>57733253</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>54</td>\n",
       "      <td>1581565745</td>\n",
       "      <td>366629</td>\n",
       "      <td>19578</td>\n",
       "      <td>273</td>\n",
       "      <td>1</td>\n",
       "      <td>1236181798</td>\n",
       "      <td>11208153</td>\n",
       "      <td>218811</td>\n",
       "      <td>4980</td>\n",
       "      <td>0</td>\n",
       "      <td>1298646801</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121386435</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>10</td>\n",
       "      <td>38691185</td>\n",
       "      <td>34413701</td>\n",
       "      <td>4769376</td>\n",
       "      <td>56375</td>\n",
       "      <td>852</td>\n",
       "      <td>51742</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.018968</td>\n",
       "      <td>0.027013</td>\n",
       "      <td>0.033453</td>\n",
       "      <td>0.032895</td>\n",
       "      <td>0.033455</td>\n",
       "      <td>0.002910</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.015606</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.000358</td>\n",
       "      <td>0.029877</td>\n",
       "      <td>0.000446</td>\n",
       "      <td>0.028221</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>0.001320</td>\n",
       "      <td>0.031730</td>\n",
       "      <td>0.031730</td>\n",
       "      <td>0.029235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hashtags  tweet_id  media  links  domains  tweet_type  language  \\\n",
       "121386431         0  57733249      5      0        0           2        54   \n",
       "121386432         0  57733250      7      0        0           1        47   \n",
       "121386433         0  57733251      1      0        0           2        13   \n",
       "121386434         0  57733252      7      0        0           1        54   \n",
       "121386435         0  57733253      5      0        0           2        54   \n",
       "\n",
       "            timestamp  a_user_id  a_follower_count  a_following_count  \\\n",
       "121386431  1581703126     534117             13919               1214   \n",
       "121386432  1582021842    2721240               186                100   \n",
       "121386433  1581734918    2023199            249849                  1   \n",
       "121386434  1581913613    2816974               517                407   \n",
       "121386435  1581565745     366629             19578                273   \n",
       "\n",
       "           a_is_verified  a_account_creation  b_user_id  b_follower_count  \\\n",
       "121386431              0          1448292186    3617447              8794   \n",
       "121386432              0          1263078566   12365145            111835   \n",
       "121386433              0          1356488269   28952089            249849   \n",
       "121386434              0          1449096567   13774342            574475   \n",
       "121386435              1          1236181798   11208153            218811   \n",
       "\n",
       "           b_following_count  b_is_verified  b_account_creation  b_follows_a  \\\n",
       "121386431               2134              0          1520948869            1   \n",
       "121386432                  3              0          1335110299            0   \n",
       "121386433                  1              0          1503940711            0   \n",
       "121386434               2656              0          1396311956            1   \n",
       "121386435               4980              0          1298646801            0   \n",
       "\n",
       "           reply  retweet  retweet_comment  like         id  tr  dt_day  \\\n",
       "121386431      0        0                0     0  121386431   1      14   \n",
       "121386432      0        0                0     0  121386432   1      18   \n",
       "121386433      0        0                0     0  121386433   1      15   \n",
       "121386434      0        0                0     0  121386434   1      17   \n",
       "121386435      0        0                0     0  121386435   1      13   \n",
       "\n",
       "           dt_dow  dt_hour  a_count_combined  a_user_fer_count_delta_time  \\\n",
       "121386431       4       17                 6                            1   \n",
       "121386432       1       10                -9                           -9   \n",
       "121386433       5        2                 0                            0   \n",
       "121386434       0        4                 2                            1   \n",
       "121386435       3        3                 0                            0   \n",
       "\n",
       "           a_user_fing_count_delta_time  a_user_fering_count_delta_time  \\\n",
       "121386431                             1                               1   \n",
       "121386432                            -9                              -9   \n",
       "121386433                             1                               0   \n",
       "121386434                             1                               1   \n",
       "121386435                             1                               0   \n",
       "\n",
       "           a_user_fing_count_mode  a_user_fer_count_mode  \\\n",
       "121386431                       0                      1   \n",
       "121386432                      -9                     -9   \n",
       "121386433                       1                      0   \n",
       "121386434                       1                      1   \n",
       "121386435                       1                      0   \n",
       "\n",
       "           a_user_fering_count_mode  count_ats  count_char  count_words  \\\n",
       "121386431                         1          0          55            5   \n",
       "121386432                        -9          0          57            5   \n",
       "121386433                         0          0          49            5   \n",
       "121386434                         1          0         104           16   \n",
       "121386435                         0          0          82           10   \n",
       "\n",
       "            tw_hash  tw_freq_hash  tw_first_word  tw_second_word  \\\n",
       "121386431  38691181      34413698         562265          296463   \n",
       "121386432  38691182      34413699        7376000         4871859   \n",
       "121386433  38691183      18372275          16591           29975   \n",
       "121386434  38691184      34413700        7376001          165976   \n",
       "121386435  38691185      34413701        4769376           56375   \n",
       "\n",
       "           tw_last_word  tw_llast_word  tw_len  tw_hash0  tw_hash1  \\\n",
       "121386431         83986         110811       3         0         0   \n",
       "121386432        348771        1398132       2         0         0   \n",
       "121386433          1205          23578       2         0         0   \n",
       "121386434          4845          16174       7         0         0   \n",
       "121386435           852          51742       3         0         0   \n",
       "\n",
       "           tw_rt_uhash  TE_b_user_id_tweet_type_language_reply  \\\n",
       "121386431            0                                     NaN   \n",
       "121386432       102048                                0.024387   \n",
       "121386433            0                                     NaN   \n",
       "121386434         2446                                0.018290   \n",
       "121386435            0                                     NaN   \n",
       "\n",
       "           TE_tw_first_word_tweet_type_language_reply  \\\n",
       "121386431                                    0.019697   \n",
       "121386432                                         NaN   \n",
       "121386433                                    0.018968   \n",
       "121386434                                         NaN   \n",
       "121386435                                    0.018968   \n",
       "\n",
       "           TE_tw_last_word_tweet_type_language_reply  \\\n",
       "121386431                                   0.020485   \n",
       "121386432                                   0.016520   \n",
       "121386433                                   0.041869   \n",
       "121386434                                   0.006793   \n",
       "121386435                                   0.027013   \n",
       "\n",
       "           TE_tw_hash0_tweet_type_language_reply  \\\n",
       "121386431                               0.033453   \n",
       "121386432                               0.005397   \n",
       "121386433                               0.042021   \n",
       "121386434                               0.008012   \n",
       "121386435                               0.033453   \n",
       "\n",
       "           TE_tw_hash1_tweet_type_language_reply  \\\n",
       "121386431                               0.032895   \n",
       "121386432                               0.005337   \n",
       "121386433                               0.043350   \n",
       "121386434                               0.007989   \n",
       "121386435                               0.032895   \n",
       "\n",
       "           TE_tw_rt_uhash_tweet_type_language_reply  TE_a_user_id_reply  \\\n",
       "121386431                                  0.033455            0.007879   \n",
       "121386432                                  0.016520            0.023279   \n",
       "121386433                                  0.042024            0.010243   \n",
       "121386434                                  0.001848            0.016004   \n",
       "121386435                                  0.033455            0.002910   \n",
       "\n",
       "           TE_b_user_id_reply  TE_tw_hash_reply  TE_tw_freq_hash_reply  \\\n",
       "121386431            0.021339               NaN                    NaN   \n",
       "121386432            0.016004               NaN                    NaN   \n",
       "121386433                 NaN               NaN               0.024387   \n",
       "121386434            0.016004               NaN                    NaN   \n",
       "121386435                 NaN               NaN                    NaN   \n",
       "\n",
       "           TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_reply  \\\n",
       "121386431                                           0.068830                            \n",
       "121386432                                           0.004327                            \n",
       "121386433                                           0.014659                            \n",
       "121386434                                           0.006315                            \n",
       "121386435                                           0.015606                            \n",
       "\n",
       "           TE_a_count_combined_tweet_type_language_reply  \\\n",
       "121386431                                       0.066553   \n",
       "121386432                                       0.005186   \n",
       "121386433                                       0.000272   \n",
       "121386434                                       0.018912   \n",
       "121386435                                       0.000105   \n",
       "\n",
       "           TE_a_user_fer_count_delta_time_media_language_reply  \\\n",
       "121386431                                           0.042014     \n",
       "121386432                                           0.012700     \n",
       "121386433                                           0.001125     \n",
       "121386434                                           0.027443     \n",
       "121386435                                           0.000358     \n",
       "\n",
       "           TE_a_user_fing_count_delta_time_media_language_reply  \\\n",
       "121386431                                           0.029877      \n",
       "121386432                                           0.012700      \n",
       "121386433                                           0.038141      \n",
       "121386434                                           0.023237      \n",
       "121386435                                           0.029877      \n",
       "\n",
       "           TE_a_user_fering_count_delta_time_tweet_type_language_reply  \\\n",
       "121386431                                           0.055450             \n",
       "121386432                                           0.005186             \n",
       "121386433                                           0.001862             \n",
       "121386434                                           0.018874             \n",
       "121386435                                           0.000446             \n",
       "\n",
       "           TE_a_user_fing_count_mode_media_language_reply  \\\n",
       "121386431                                        0.002924   \n",
       "121386432                                        0.012700   \n",
       "121386433                                        0.037599   \n",
       "121386434                                        0.022679   \n",
       "121386435                                        0.028221   \n",
       "\n",
       "           TE_a_user_fer_count_mode_media_language_reply  \\\n",
       "121386431                                       0.040001   \n",
       "121386432                                       0.012700   \n",
       "121386433                                       0.001141   \n",
       "121386434                                       0.026802   \n",
       "121386435                                       0.001001   \n",
       "\n",
       "           TE_a_user_fering_count_mode_tweet_type_language_reply  \\\n",
       "121386431                                           0.053399       \n",
       "121386432                                           0.005186       \n",
       "121386433                                           0.002460       \n",
       "121386434                                           0.017103       \n",
       "121386435                                           0.001320       \n",
       "\n",
       "           TE_domains_media_tweet_type_language_reply  \\\n",
       "121386431                                    0.031730   \n",
       "121386432                                    0.004740   \n",
       "121386433                                    0.038487   \n",
       "121386434                                    0.005846   \n",
       "121386435                                    0.031730   \n",
       "\n",
       "           TE_links_media_tweet_type_language_reply  \\\n",
       "121386431                                  0.031730   \n",
       "121386432                                  0.004740   \n",
       "121386433                                  0.038487   \n",
       "121386434                                  0.005846   \n",
       "121386435                                  0.031730   \n",
       "\n",
       "           TE_hashtags_media_tweet_type_language_reply  \n",
       "121386431                                     0.029235  \n",
       "121386432                                     0.005287  \n",
       "121386433                                     0.044228  \n",
       "121386434                                     0.006122  \n",
       "121386435                                     0.029235  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtest = xgb.DMatrix(data=test0.drop(RMV, axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12434735"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = joblib.load( 'model-'+TARGET+'-1.xgb')\n",
    "ytest = model.predict(dtest,ntree_limit=500)\n",
    "del model; gc.collect()\n",
    "len(ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12434735"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = joblib.load( 'model-'+TARGET+'-2.xgb' )\n",
    "ytest += model.predict(dtest)\n",
    "ytest /= 2.\n",
    "del model, dtest; gc.collect()\n",
    "len(ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dt_day\n",
      "13    0.025698\n",
      "14    0.019684\n",
      "15    0.022726\n",
      "16    0.025837\n",
      "17    0.028258\n",
      "18    0.024089\n",
      "19    0.024276\n",
      "Name: ypred, dtype: float32\n",
      "0.029029367 0.024106601 0.02362082 0.02617657 1.2042083 1.108199 1.1562036385083547\n"
     ]
    }
   ],
   "source": [
    "test0['ypred'] = ytest\n",
    "\n",
    "print( test0.groupby('dt_day')['ypred'].mean() )\n",
    "m17 = test0.loc[ (test0.dt_day==17)&(test0.dt_hour==23), 'ypred' ].mean()\n",
    "m18a = test0.loc[ (test0.dt_day==18)&(test0.dt_hour==0), 'ypred' ].mean()\n",
    "m18b = test0.loc[ (test0.dt_day==18)&(test0.dt_hour==23), 'ypred' ].mean()\n",
    "m19 = test0.loc[ (test0.dt_day==19)&(test0.dt_hour==0), 'ypred' ].mean()\n",
    "\n",
    "print( m17, m18a, m18b, m19, m17/m18a, m19/m18b,0.5*m17/m18a+ 0.5*m19/m18b )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.024963805"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test0.loc[ test0.dt_day==18 ,'ypred'] *=  ( 0.5*m17/m18a+ 0.5*m19/m18b )\n",
    "\n",
    "sub['prediction'] = test0['ypred'].values\n",
    "sub.to_csv('submissions/xgb-final-'+TARGET+'-public-1.csv',header=None, index=False)\n",
    "sub['prediction'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Private competition_test.tsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "test1.sort_values( 'id', inplace=True )\n",
    "sub = pd.read_csv('../preprocessings/sample_submission_private.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hashtags</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>media</th>\n",
       "      <th>links</th>\n",
       "      <th>domains</th>\n",
       "      <th>tweet_type</th>\n",
       "      <th>language</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>a_user_id</th>\n",
       "      <th>a_follower_count</th>\n",
       "      <th>a_following_count</th>\n",
       "      <th>a_is_verified</th>\n",
       "      <th>a_account_creation</th>\n",
       "      <th>b_user_id</th>\n",
       "      <th>b_follower_count</th>\n",
       "      <th>b_following_count</th>\n",
       "      <th>b_is_verified</th>\n",
       "      <th>b_account_creation</th>\n",
       "      <th>b_follows_a</th>\n",
       "      <th>reply</th>\n",
       "      <th>retweet</th>\n",
       "      <th>retweet_comment</th>\n",
       "      <th>like</th>\n",
       "      <th>id</th>\n",
       "      <th>tr</th>\n",
       "      <th>dt_day</th>\n",
       "      <th>dt_dow</th>\n",
       "      <th>dt_hour</th>\n",
       "      <th>a_count_combined</th>\n",
       "      <th>a_user_fer_count_delta_time</th>\n",
       "      <th>a_user_fing_count_delta_time</th>\n",
       "      <th>a_user_fering_count_delta_time</th>\n",
       "      <th>a_user_fing_count_mode</th>\n",
       "      <th>a_user_fer_count_mode</th>\n",
       "      <th>a_user_fering_count_mode</th>\n",
       "      <th>count_ats</th>\n",
       "      <th>count_char</th>\n",
       "      <th>count_words</th>\n",
       "      <th>tw_hash</th>\n",
       "      <th>tw_freq_hash</th>\n",
       "      <th>tw_first_word</th>\n",
       "      <th>tw_second_word</th>\n",
       "      <th>tw_last_word</th>\n",
       "      <th>tw_llast_word</th>\n",
       "      <th>tw_len</th>\n",
       "      <th>tw_hash0</th>\n",
       "      <th>tw_hash1</th>\n",
       "      <th>tw_rt_uhash</th>\n",
       "      <th>TE_b_user_id_tweet_type_language_reply</th>\n",
       "      <th>TE_tw_first_word_tweet_type_language_reply</th>\n",
       "      <th>TE_tw_last_word_tweet_type_language_reply</th>\n",
       "      <th>TE_tw_hash0_tweet_type_language_reply</th>\n",
       "      <th>TE_tw_hash1_tweet_type_language_reply</th>\n",
       "      <th>TE_tw_rt_uhash_tweet_type_language_reply</th>\n",
       "      <th>TE_a_user_id_reply</th>\n",
       "      <th>TE_b_user_id_reply</th>\n",
       "      <th>TE_tw_hash_reply</th>\n",
       "      <th>TE_tw_freq_hash_reply</th>\n",
       "      <th>TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_reply</th>\n",
       "      <th>TE_a_count_combined_tweet_type_language_reply</th>\n",
       "      <th>TE_a_user_fer_count_delta_time_media_language_reply</th>\n",
       "      <th>TE_a_user_fing_count_delta_time_media_language_reply</th>\n",
       "      <th>TE_a_user_fering_count_delta_time_tweet_type_language_reply</th>\n",
       "      <th>TE_a_user_fing_count_mode_media_language_reply</th>\n",
       "      <th>TE_a_user_fer_count_mode_media_language_reply</th>\n",
       "      <th>TE_a_user_fering_count_mode_tweet_type_language_reply</th>\n",
       "      <th>TE_domains_media_tweet_type_language_reply</th>\n",
       "      <th>TE_links_media_tweet_type_language_reply</th>\n",
       "      <th>TE_hashtags_media_tweet_type_language_reply</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>11867</td>\n",
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       "      <td>0.066342</td>\n",
       "      <td>0.054597</td>\n",
       "      <td>0.051955</td>\n",
       "      <td>0.039584</td>\n",
       "      <td>0.054543</td>\n",
       "      <td>0.037854</td>\n",
       "      <td>0.049571</td>\n",
       "      <td>0.052935</td>\n",
       "      <td>0.047789</td>\n",
       "      <td>0.047789</td>\n",
       "      <td>0.043406</td>\n",
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       "    <tr>\n",
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       "      <td>1494251627</td>\n",
       "      <td>879586</td>\n",
       "      <td>4312</td>\n",
       "      <td>660</td>\n",
       "      <td>0</td>\n",
       "      <td>1540395738</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>133821168</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>0</td>\n",
       "      <td>129</td>\n",
       "      <td>18</td>\n",
       "      <td>45410186</td>\n",
       "      <td>40509200</td>\n",
       "      <td>7627046</td>\n",
       "      <td>1151943</td>\n",
       "      <td>2638</td>\n",
       "      <td>18369</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>139857</td>\n",
       "      <td>0.023279</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006459</td>\n",
       "      <td>0.008012</td>\n",
       "      <td>0.007989</td>\n",
       "      <td>0.011381</td>\n",
       "      <td>0.003909</td>\n",
       "      <td>0.017071</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005881</td>\n",
       "      <td>0.008419</td>\n",
       "      <td>0.016969</td>\n",
       "      <td>0.016969</td>\n",
       "      <td>0.008419</td>\n",
       "      <td>0.016969</td>\n",
       "      <td>0.016969</td>\n",
       "      <td>0.008419</td>\n",
       "      <td>0.005898</td>\n",
       "      <td>0.005898</td>\n",
       "      <td>0.005893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133821169</th>\n",
       "      <td>26906</td>\n",
       "      <td>66410932</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1582083666</td>\n",
       "      <td>924614</td>\n",
       "      <td>272</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "      <td>1559086871</td>\n",
       "      <td>647103</td>\n",
       "      <td>272</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "      <td>1432084055</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>133821169</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>9</td>\n",
       "      <td>39837068</td>\n",
       "      <td>35471058</td>\n",
       "      <td>6610718</td>\n",
       "      <td>1008990</td>\n",
       "      <td>69708</td>\n",
       "      <td>35969</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>364477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.015063</td>\n",
       "      <td>0.003214</td>\n",
       "      <td>0.003181</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.004656</td>\n",
       "      <td>0.044474</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.003759</td>\n",
       "      <td>0.006054</td>\n",
       "      <td>0.025392</td>\n",
       "      <td>0.020168</td>\n",
       "      <td>0.006042</td>\n",
       "      <td>0.019874</td>\n",
       "      <td>0.024744</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>0.003333</td>\n",
       "      <td>0.003333</td>\n",
       "      <td>0.019697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133821170</th>\n",
       "      <td>518727</td>\n",
       "      <td>66410933</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>1581779241</td>\n",
       "      <td>4280276</td>\n",
       "      <td>1020</td>\n",
       "      <td>2097</td>\n",
       "      <td>0</td>\n",
       "      <td>1468438879</td>\n",
       "      <td>29849501</td>\n",
       "      <td>405774</td>\n",
       "      <td>974</td>\n",
       "      <td>0</td>\n",
       "      <td>1385502405</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>133821170</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>5</td>\n",
       "      <td>167</td>\n",
       "      <td>24</td>\n",
       "      <td>45410187</td>\n",
       "      <td>40509201</td>\n",
       "      <td>7773604</td>\n",
       "      <td>5355936</td>\n",
       "      <td>3030</td>\n",
       "      <td>2103</td>\n",
       "      <td>9</td>\n",
       "      <td>6521258</td>\n",
       "      <td>5953099</td>\n",
       "      <td>325751</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.015299</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.022267</td>\n",
       "      <td>0.021339</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006659</td>\n",
       "      <td>0.003361</td>\n",
       "      <td>0.015112</td>\n",
       "      <td>0.021440</td>\n",
       "      <td>0.002084</td>\n",
       "      <td>0.021915</td>\n",
       "      <td>0.018948</td>\n",
       "      <td>0.002279</td>\n",
       "      <td>0.007238</td>\n",
       "      <td>0.007238</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hashtags  tweet_id  media  links  domains  tweet_type  language  \\\n",
       "133821166         0  66410929      0      0        0           2        11   \n",
       "133821167     25339  66410930      0      0        0           1        11   \n",
       "133821168         0  66410931      9      0        0           1        54   \n",
       "133821169     26906  66410932      5      0        0           1         4   \n",
       "133821170    518727  66410933      5      0        0           1        54   \n",
       "\n",
       "            timestamp  a_user_id  a_follower_count  a_following_count  \\\n",
       "133821166  1581759640   12167358               119                125   \n",
       "133821167  1581668217    9371104               189                264   \n",
       "133821168  1582046459     199258              4312                660   \n",
       "133821169  1582083666     924614               272                185   \n",
       "133821170  1581779241    4280276              1020               2097   \n",
       "\n",
       "           a_is_verified  a_account_creation  b_user_id  b_follower_count  \\\n",
       "133821166              0          1571666822    6237935               189   \n",
       "133821167              0          1575966890    6237935               189   \n",
       "133821168              0          1494251627     879586              4312   \n",
       "133821169              0          1559086871     647103               272   \n",
       "133821170              0          1468438879   29849501            405774   \n",
       "\n",
       "           b_following_count  b_is_verified  b_account_creation  b_follows_a  \\\n",
       "133821166                264              0          1478011810            1   \n",
       "133821167                264              0          1478011810            1   \n",
       "133821168                660              0          1540395738            1   \n",
       "133821169                185              0          1432084055            1   \n",
       "133821170                974              0          1385502405            0   \n",
       "\n",
       "           reply  retweet  retweet_comment  like         id  tr  dt_day  \\\n",
       "133821166      0        0                0     0  133821166   2      15   \n",
       "133821167      0        0                0     0  133821167   2      14   \n",
       "133821168      0        0                0     0  133821168   2      18   \n",
       "133821169      0        0                0     0  133821169   2      19   \n",
       "133821170      0        0                0     0  133821170   2      15   \n",
       "\n",
       "           dt_dow  dt_hour  a_count_combined  a_user_fer_count_delta_time  \\\n",
       "133821166       5        9                 2                            1   \n",
       "133821167       4        8                 2                            1   \n",
       "133821168       1       17                -9                           -9   \n",
       "133821169       2        3                 2                            1   \n",
       "133821170       5       15                10                           -1   \n",
       "\n",
       "           a_user_fing_count_delta_time  a_user_fering_count_delta_time  \\\n",
       "133821166                             1                               1   \n",
       "133821167                             1                               1   \n",
       "133821168                            -9                              -9   \n",
       "133821169                             1                               1   \n",
       "133821170                            -1                              -1   \n",
       "\n",
       "           a_user_fing_count_mode  a_user_fer_count_mode  \\\n",
       "133821166                       1                      1   \n",
       "133821167                       1                      1   \n",
       "133821168                      -9                     -9   \n",
       "133821169                       1                      1   \n",
       "133821170                      -1                     -1   \n",
       "\n",
       "           a_user_fering_count_mode  count_ats  count_char  count_words  \\\n",
       "133821166                         1          0          59           15   \n",
       "133821167                         1          0         189           36   \n",
       "133821168                        -9          0         129           18   \n",
       "133821169                         1          0          73            9   \n",
       "133821170                        -1          5         167           24   \n",
       "\n",
       "            tw_hash  tw_freq_hash  tw_first_word  tw_second_word  \\\n",
       "133821166  45410185      40509199         163328           11867   \n",
       "133821167  43004406      38346818        4631037          288783   \n",
       "133821168  45410186      40509200        7627046         1151943   \n",
       "133821169  39837068      35471058        6610718         1008990   \n",
       "133821170  45410187      40509201        7773604         5355936   \n",
       "\n",
       "           tw_last_word  tw_llast_word  tw_len  tw_hash0  tw_hash1  \\\n",
       "133821166          5615           8175       5         0         0   \n",
       "133821167           808            902      11         0         0   \n",
       "133821168          2638          18369       8         0         0   \n",
       "133821169         69708          35969       3         0         0   \n",
       "133821170          3030           2103       9   6521258   5953099   \n",
       "\n",
       "           tw_rt_uhash  TE_b_user_id_tweet_type_language_reply  \\\n",
       "133821166            0                                0.093042   \n",
       "133821167      3665864                                     NaN   \n",
       "133821168       139857                                0.023279   \n",
       "133821169       364477                                     NaN   \n",
       "133821170       325751                                     NaN   \n",
       "\n",
       "           TE_tw_first_word_tweet_type_language_reply  \\\n",
       "133821166                                    0.066973   \n",
       "133821167                                    0.022267   \n",
       "133821168                                         NaN   \n",
       "133821169                                         NaN   \n",
       "133821170                                         NaN   \n",
       "\n",
       "           TE_tw_last_word_tweet_type_language_reply  \\\n",
       "133821166                                   0.034903   \n",
       "133821167                                   0.005953   \n",
       "133821168                                   0.006459   \n",
       "133821169                                   0.015063   \n",
       "133821170                                   0.015299   \n",
       "\n",
       "           TE_tw_hash0_tweet_type_language_reply  \\\n",
       "133821166                               0.033600   \n",
       "133821167                               0.005486   \n",
       "133821168                               0.008012   \n",
       "133821169                               0.003214   \n",
       "133821170                                    NaN   \n",
       "\n",
       "           TE_tw_hash1_tweet_type_language_reply  \\\n",
       "133821166                               0.034768   \n",
       "133821167                               0.005423   \n",
       "133821168                               0.007989   \n",
       "133821169                               0.003181   \n",
       "133821170                                    NaN   \n",
       "\n",
       "           TE_tw_rt_uhash_tweet_type_language_reply  TE_a_user_id_reply  \\\n",
       "133821166                                  0.033600            0.023279   \n",
       "133821167                                       NaN            0.022267   \n",
       "133821168                                  0.011381            0.003909   \n",
       "133821169                                       NaN            0.004656   \n",
       "133821170                                  0.022267            0.021339   \n",
       "\n",
       "           TE_b_user_id_reply  TE_tw_hash_reply  TE_tw_freq_hash_reply  \\\n",
       "133821166            0.089719               NaN                    NaN   \n",
       "133821167            0.089719               NaN                    NaN   \n",
       "133821168            0.017071               NaN                    NaN   \n",
       "133821169            0.044474               NaN                    NaN   \n",
       "133821170                 NaN               NaN                    NaN   \n",
       "\n",
       "           TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_reply  \\\n",
       "133821166                                           0.066342                            \n",
       "133821167                                           0.006076                            \n",
       "133821168                                           0.005881                            \n",
       "133821169                                           0.003759                            \n",
       "133821170                                           0.006659                            \n",
       "\n",
       "           TE_a_count_combined_tweet_type_language_reply  \\\n",
       "133821166                                       0.054597   \n",
       "133821167                                       0.013031   \n",
       "133821168                                       0.008419   \n",
       "133821169                                       0.006054   \n",
       "133821170                                       0.003361   \n",
       "\n",
       "           TE_a_user_fer_count_delta_time_media_language_reply  \\\n",
       "133821166                                           0.051955     \n",
       "133821167                                           0.051955     \n",
       "133821168                                           0.016969     \n",
       "133821169                                           0.025392     \n",
       "133821170                                           0.015112     \n",
       "\n",
       "           TE_a_user_fing_count_delta_time_media_language_reply  \\\n",
       "133821166                                           0.039584      \n",
       "133821167                                           0.039584      \n",
       "133821168                                           0.016969      \n",
       "133821169                                           0.020168      \n",
       "133821170                                           0.021440      \n",
       "\n",
       "           TE_a_user_fering_count_delta_time_tweet_type_language_reply  \\\n",
       "133821166                                           0.054543             \n",
       "133821167                                           0.013104             \n",
       "133821168                                           0.008419             \n",
       "133821169                                           0.006042             \n",
       "133821170                                           0.002084             \n",
       "\n",
       "           TE_a_user_fing_count_mode_media_language_reply  \\\n",
       "133821166                                        0.037854   \n",
       "133821167                                        0.037854   \n",
       "133821168                                        0.016969   \n",
       "133821169                                        0.019874   \n",
       "133821170                                        0.021915   \n",
       "\n",
       "           TE_a_user_fer_count_mode_media_language_reply  \\\n",
       "133821166                                       0.049571   \n",
       "133821167                                       0.049571   \n",
       "133821168                                       0.016969   \n",
       "133821169                                       0.024744   \n",
       "133821170                                       0.018948   \n",
       "\n",
       "           TE_a_user_fering_count_mode_tweet_type_language_reply  \\\n",
       "133821166                                           0.052935       \n",
       "133821167                                           0.012100       \n",
       "133821168                                           0.008419       \n",
       "133821169                                           0.005747       \n",
       "133821170                                           0.002279       \n",
       "\n",
       "           TE_domains_media_tweet_type_language_reply  \\\n",
       "133821166                                    0.047789   \n",
       "133821167                                    0.005484   \n",
       "133821168                                    0.005898   \n",
       "133821169                                    0.003333   \n",
       "133821170                                    0.007238   \n",
       "\n",
       "           TE_links_media_tweet_type_language_reply  \\\n",
       "133821166                                  0.047789   \n",
       "133821167                                  0.005484   \n",
       "133821168                                  0.005898   \n",
       "133821169                                  0.003333   \n",
       "133821170                                  0.007238   \n",
       "\n",
       "           TE_hashtags_media_tweet_type_language_reply  \n",
       "133821166                                     0.043406  \n",
       "133821167                                     0.011910  \n",
       "133821168                                     0.005893  \n",
       "133821169                                     0.019697  \n",
       "133821170                                          NaN  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtest = xgb.DMatrix(data=test1.drop(RMV, axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12434838"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = joblib.load( 'model-'+TARGET+'-1.xgb' )\n",
    "ytest = model.predict(dtest)\n",
    "del model; gc.collect()\n",
    "len(ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12434838"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = joblib.load( 'model-'+TARGET+'-2.xgb' )\n",
    "ytest += model.predict(dtest)\n",
    "ytest /= 2.\n",
    "del model, dtest; gc.collect()\n",
    "len(ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dt_day\n",
      "13    0.025979\n",
      "14    0.019875\n",
      "15    0.022979\n",
      "16    0.026133\n",
      "17    0.028665\n",
      "18    0.024450\n",
      "19    0.024573\n",
      "Name: ypred, dtype: float32\n",
      "0.029194945 0.024142366 0.024097705 0.026706325 1.2092828 1.1082518 1.1587672439067762\n"
     ]
    }
   ],
   "source": [
    "test1['ypred'] = ytest\n",
    "\n",
    "print( test1.groupby('dt_day')['ypred'].mean() )\n",
    "\n",
    "m17 = test1.loc[ (test1.dt_day==17)&(test1.dt_hour==23), 'ypred' ].mean()\n",
    "m18a = test1.loc[ (test1.dt_day==18)&(test1.dt_hour==0), 'ypred' ].mean()\n",
    "m18b = test1.loc[ (test1.dt_day==18)&(test1.dt_hour==23), 'ypred' ].mean()\n",
    "m19 = test1.loc[ (test1.dt_day==19)&(test1.dt_hour==0), 'ypred' ].mean()\n",
    "print( m17, m18a, m18b, m19, m17/m18a, m19/m18b, 0.5*m17/m18a+ 0.5*m19/m18b )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.025279723"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test1.loc[ test1.dt_day==18 ,'ypred'] *= ( 0.5*m17/m18a+ 0.5*m19/m18b )\n",
    "\n",
    "sub['prediction'] = test1['ypred'].values\n",
    "sub.to_csv('submissions/xgb-'+TARGET+'-private-1.csv',header=None, index=False)\n",
    "sub['prediction'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7edd947a7cc0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt0 = test0.groupby( ['dt_day','dt_hour'] )['ypred'].agg('mean').reset_index()\n",
    "dt1 = test1.groupby( ['dt_day','dt_hour'] )['ypred'].agg('mean').reset_index()\n",
    "dt0.sort_values(['dt_day','dt_hour'], inplace=True)\n",
    "dt1.sort_values(['dt_day','dt_hour'], inplace=True)\n",
    "\n",
    "dt0['ypred2'] = dt1['ypred'].values\n",
    "\n",
    "dt0[['ypred','ypred2']].plot(  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed Time is 51.394057 minutes\n"
     ]
    }
   ],
   "source": [
    "print('Elapsed Time is %f minutes'%((time.time()-startNB)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
